---
language:
- en
license: cc-by-nc-4.0
library_name: peft
tags:
- medical
pipeline_tag: text-generation
base_model: tiiuae/falcon-40b
---

# MedFalcon v2 40b LoRA - Final

![Screenshot](https://huggingface.co./nmitchko/medfalcon-v2-40b-lora/resolve/main/Screenshot%202023-07-21%20102515.png)

## Model Description

This a model release at `1 epoch`. For evaluation use only! Limitations: 
* Do not use to treat patients! Treat AI content as if you wrote it!!!

### Architecture
`nmitchko/medfalcon-v2-40b-lora` is a large language model LoRa specifically fine-tuned for medical domain tasks.
It is based on [`Falcon-40b`](https://huggingface.co./tiiuae/falcon-40b) at 40 billion parameters.

The primary goal of this model is to improve question-answering and medical dialogue tasks.
It was trained using [LoRA](https://arxiv.org/abs/2106.09685), specifically [QLora](https://github.com/artidoro/qlora), to reduce memory footprint. 

See Training Parameters for more info  This Lora supports 4-bit and 8-bit modes.

### Requirements

```
bitsandbytes>=0.39.0
peft
transformers
```

Steps to load this model:
1. Load base model using transformers
2. Apply LoRA using peft

```python
# 
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
from peft import PeftModel

model = "tiiuae/falcon-40b"
LoRA = "nmitchko/medfalcon-v2-40b-lora"

# If you want 8 or 4 bit set the appropriate flags
load_8bit = True

tokenizer = AutoTokenizer.from_pretrained(model)

model = AutoModelForCausalLM.from_pretrained(model,
    load_in_8bit=load_8bit,
    torch_dtype=torch.float16,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(model, LoRA)

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

sequences = pipeline(
   "What does the drug ceftrioxone do?\nDoctor:",
    max_length=200,
    do_sample=True,
    top_k=40,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)

for seq in sequences:
    print(f"Result: {seq['generated_text']}")
```

## Training Parameters 

The model was trained for or 1 epoch on a custom, unreleased dataset named `medconcat`. 
`medconcat` contains only human generated content and weighs in at over 100MiB of raw text.  


| Item          | Amount | Units |
|---------------|--------|-------|
| LoRA Rank     | 64    | ~     |
| LoRA Alpha    | 16    | ~     |
| Learning Rate | 1e-4   | SI    |
| Dropout       | 5      | %     |